Abstract. Colorectal cancer (CRC) is among the main tumor-related causes of death worldwide. The fact that the majority of the patients develop resistance to chemoradiotherapy (CRT) is a major obstacle for the treatment of CRC. In order to develop more effective treatment strategies, it is crucial to elucidate the mechanisms underlying the development of resistance to CRT. Several studies have recently indicated the regulatory effects of microRNAs (miRNAs) in response to antitumor agents. For example, miR-34a attenuates the chemoresistance of colon cancer to 5-FU by inhibiting E2F3 and SIRT1. The miR-34a mimic MRX34 is the first synthetic miRNA to have been entered into clinical trials. miR-21 prevents tumor cell stemness, invasion and drug resistance, which are required for the development of CRC. These findings suggest that miRNAs represent a focus in the research of novel cancer treatments aimed at sensitizing cancer cells to chemotherapeutic drugs. The aim of the present study was to review the functions of miRNAs and investigate the roles of miRNAs in CRC radioresistance or chemoresistance. Furthermore, the potential of including miRNAs in therapeutic strategies and using them as molecular biomarkers for predicting radiosensitivity and chemosensitivity was discussed.

Background Sorafenib resistance is a key impediment to successful treatment of patients with advanced hepatocellular carcinoma (HCC) and recent studies have reported reversal of drug resistance by targeting ferroptosis. The present study aimed to explore the association of fatty acid synthase (FASN) with sorafenib resistance via regulation of ferroptosis and provide a novel treatment strategy to overcome the sorafenib resistance of HCC patients. Methods Intracellular levels of lipid peroxides, glutathione, malondialdehyde, and Fe2+ were measured as indicators of ferroptosis status. Biological information analyses, immunofluorescence assays, western blot assays, and co-immunoprecipitation analyses were conducted to elucidate the functions of FASN in HCC. Both in vitro and in vivo studies were conducted to examine the antitumor effects of the combination of orlistat and sorafenib and CalcuSyn software was used to calculate the combination index. Results Solute carrier family 7 member 11 (SLC7A11) was found to play an important role in mediating sorafenib resistance. The up-regulation of FASN antagonize of SLC7A11-mediated ferroptosis and thereby promoted sorafenib resistance. Mechanistically, FASN enhanced sorafenib-induced ferroptosis resistance by binding to hypoxia-inducible factor 1-alpha (HIF1α), promoting HIF1α nuclear translocation, inhibiting ubiquitination and proteasomal degradation of HIF1α, and subsequently enhancing transcription of SLC7A11. Orlistat, an inhibitor of FASN, with sorafenib had significant synergistic antitumor effects and reversed sorafenib resistance both in vitro and in vivo. Conclusion Targeting the FASN/HIF1α/SLC7A11 pathway resensitized HCC cells to sorafenib. The combination of orlistat and sorafenib had superior synergistic antitumor effects in sorafenib-resistant HCC cells.

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BackgroundBecause of the superficial and infiltrative spreading patterns of esophageal squamous cell carcinoma (ESCC), an accurate assessment of tumor extent is challenging using imaging-based clinical staging. Radiomics features extracted from pretreatment computed tomography (CT) or magnetic resonance imaging have shown promise in identifying tumor characteristics. Accurate staging is essential for planning cancer treatment, especially for deciding whether to offer surgery or radiotherapy (chemotherapy) in patients with locally advanced ESCC. Thus, this study aimed to evaluate the predictive potential of contrast-enhanced CT-based radiomics as a non-invasive approach for estimating pathological tumor extent in ESCC patients.MethodsPatients who underwent esophagectomy between October 2011 and September 2017 were retrospectively studied and included 116 patients with pathologically confirmed ESCC. Contrast-enhanced CT from the neck to the abdomen was performed in all patients during the 2 weeks before the operation. Radiomics features were extracted from segmentations, which were contoured by radiologists. Cluster analysis was performed to obtain clusters with similar radiomics characteristics, and chi-squared tests were used to assess differences in clinicopathological features and survival among clusters. Furthermore, a least absolute shrinkage and selection operator was performed to select radiomics features and construct a radiomics model. Receiver operating characteristic analysis was used to evaluate the predictive ability of the radiomics signatures.ResultsAll 116 ESCC patients were divided into two groups according to the cluster analysis. The chi-squared test showed that cluster-based radiomics features were significantly correlated with T stage (p = 0.0254) and tumor length (p = 0.0002). Furthermore, CT radiomics signatures exhibited favorable predictive performance for T stage (area under the curve [AUC] = 0.86, sensitivity = 0.77, and specificity = 0.87) and tumor length (AUC = 0.95, sensitivity = 0.92, and specificity = 0.91).ConclusionsCT contrast radiomics is a simple and non-invasive method that shows promise for predicting pathological T stage and tumor length preoperatively in ESCC patients and may aid in the accurate assessments of patients in combination with the existing examinations.

Introduction: Left ventricular reverse remodeling (LVRR) is associated with decreased cardiovascular mortality and improved cardiac survival and also crucial for therapeutic options. However, there is a lack of an early prediction model of LVRR in first-diagnosed dilated cardiomyopathy.Methods: This single-center study included 104 patients with idiopathic DCM. We defined LVRR as an absolute increase in left ventricular ejection fraction (LVEF) from >10% to a final value >35% and a decrease in left ventricular end-diastolic diameter (LVDd) >10%. Analysis features included demographic characteristics, comorbidities, physical sign, biochemistry data, echocardiography, electrocardiogram, Holter monitoring, and medication. Logistic regression, random forests, and extreme gradient boosting (XGBoost) were, respectively, implemented in a 10-fold cross-validated model to discriminate LVRR and non-LVRR, with receiver operating characteristic (ROC) curves and calibration plot for performance evaluation.Results: LVRR occurred in 47 (45.2%) patients after optimal medical treatment. Cystatin C, right ventricular end-diastolic dimension, high-density lipoprotein cholesterol (HDL-C), left atrial dimension, left ventricular posterior wall dimension, systolic blood pressure, severe mitral regurgitation, eGFR, and NYHA classification were included in XGBoost, which reached higher AU-ROC compared with logistic regression (AU-ROC, 0.8205 vs. 0.5909, p = 0.0119). Ablation analysis revealed that cystatin C, right ventricular end-diastolic dimension, and HDL-C made the largest contributions to the model.Conclusion: Tree-based models like XGBoost were able to early differentiate LVRR and non-LVRR in patients with first-diagnosed DCM before drug therapy, facilitating disease management and invasive therapy selection. A multicenter prospective study is necessary for further validation.Clinical Trial Registration:http://www.chictr.org.cn/usercenter.aspx (ChiCTR2000034128).